TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval

• URL: http://arxiv.org/abs/2506.09114v1
• Date: Tue, 10 Jun 2025 17:59:56 GMT
• Title: TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval
• Authors: Jialin Chen, Ziyu Zhao, Gaukhar Nurbek, Aosong Feng, Ali Maatouk, Leandros Tassiulas, Yifeng Gao, Rex Ying,
• Abstract summary: TRACE is a generic multimodal retriever that grounds time-series embeddings in aligned textual context.<n>It supports flexible cross-modal retrieval modes, including Text-to-Timeseries and Timeseries-to-Text.<n>TRACE also serves as a powerful standalone encoder, with lightweight task-specific tuning that refines context-aware representations.
• Score: 22.412759868927118
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The ubiquity of dynamic data in domains such as weather, healthcare, and energy underscores a growing need for effective interpretation and retrieval of time-series data. These data are inherently tied to domain-specific contexts, such as clinical notes or weather narratives, making cross-modal retrieval essential not only for downstream tasks but also for developing robust time-series foundation models by retrieval-augmented generation (RAG). Despite the increasing demand, time-series retrieval remains largely underexplored. Existing methods often lack semantic grounding, struggle to align heterogeneous modalities, and have limited capacity for handling multi-channel signals. To address this gap, we propose TRACE, a generic multimodal retriever that grounds time-series embeddings in aligned textual context. TRACE enables fine-grained channel-level alignment and employs hard negative mining to facilitate semantically meaningful retrieval. It supports flexible cross-modal retrieval modes, including Text-to-Timeseries and Timeseries-to-Text, effectively linking linguistic descriptions with complex temporal patterns. By retrieving semantically relevant pairs, TRACE enriches downstream models with informative context, leading to improved predictive accuracy and interpretability. Beyond a static retrieval engine, TRACE also serves as a powerful standalone encoder, with lightweight task-specific tuning that refines context-aware representations while maintaining strong cross-modal alignment. These representations achieve state-of-the-art performance on downstream forecasting and classification tasks. Extensive experiments across multiple domains highlight its dual utility, as both an effective encoder for downstream applications and a general-purpose retriever to enhance time-series models.

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